Abstract
Analyzing complex problem domains is not easy. Simulation tools support decision makers to find the best policies. Model-based system development is an approach where a model of the application domain is the central driving force when designing simulation tools. State-of-the-art techniques however still require both expert knowledge of the application domain and the implementation techniques as provided by ICT (such as multilevel agent technology). Domain experts, however, usually do not master ICT sufficiently. Modeling is more insightful for the domain expert when its goal is to formalize the language being used in that domain as a semi-natural language. At the meta level, this language describes the main concepts of the type of application domain. The model then is a concretization of this meta model. The main focus of this article is (1) to propose a natural-language-based approach to modeling application domains, (2) to show how these models can be transformed systematically into computational models, and (3) to propose the tool TiC (Tool in Context) that supports the domain expert when developing a model and generating a simulation tool. Our research methodology is based on Design Science. We verify our approach by describing the various transformation steps in detail, and by demonstrating the way of working via a sample session applying a real problem of Laf Forest Reserve deforestation in North Cameroon.
Highlights
Complex systems comprise a large number of interacting elements whose overall characteristics cannot be deduced directly from their components
The methodology we use is based on the Design Science Research Method (DSRM) which is the standard research methodology used in the Information Systems discipline for designing new artifacts that solve unsolved problems or improve upon existing solutions
We have shown how a conceptual domain model in general, and the AiC meta-model in particular, can be transformed via a relational model into an XText description, which is the basis of generating various tools such as a syntax driven editor and the extensions described in previous section
Summary
Complex systems comprise a large number of interacting elements whose overall characteristics cannot be deduced directly from their components. Modeling and simulation often require a number of skills that are not readily mastered by domain experts such as social scientists, geographers, and biologists. This necessitates these researchers to enter into dialogue with ICT experts, often finding the gap between the domain’s and the ICT languages and methods to bridge. This interdisciplinary approach is increasingly observed in Agent-Based Models (ABM), enabling explicit representation of interactions and providing a framework for taking into account the dynamics of systems and the individual or collective logic of Decision Making. Challenges related to conceptual foundations can be regarded as mathematical and analytical; challenges related to implementation can be regarded as computational or, more precisely, as related to computer science [24]
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